HiFlight

HiFlight’s mission is to build autonomous data infrastructure that eliminates the need for manual pipeline engineering. Today, organizations spend significant time writing, maintaining, and fixing ELT pipelines. HiFlight introduces an AI-native system that automatically builds, monitors, and repairs data workflows in real time. By shifting from human-managed to self-maintaining pipelines, we enable faster, more reliable access to data and reduce technical barriers. Our mission is to enable any team, regardless of access to engineering resources, to build production-grade data systems.

Modern companies rely on data to drive decisions, yet the infrastructure required to produce reliable data is still complex, fragile, and heavily dependent on engineers. Current ELT tools like Fivetran, DBT, and Informatica have improved accessibility but still require engineers to design, maintain, and repair pipelines. So whenever schema drifts occur or data sources break, ELT pipelines fail and must be manually debugged, rewired, and redeployed, creating significant delays and limiting access to reliable data systems. The market for ELT is huge ($8.85 billion in 2025) and growing rapidly, projected to exceed $18 billion by 2030. As companies ingress more data from different sources and operate in real time, pipeline complexity continues to increase. The ELT market is also seeing a shift towards no-code and AI-driven tools, indicating a strong market pull for more automated solutions. HiFlight addresses this gap in the ELT market by introducing an AI-native solution to data infrastructure. Instead of assisting engineers, HiFlight replaces the need for manual pipeline management entirely. Engineers must specify a policy engine and set up the initial pipeline destinations and sources. From there, HiFlight’s AI agent manages the full lifecycle of pipelines, from detecting schema drift, generating transformations, validating outputs, and deploying fixes, revolutionizing the ELT space from the existing static systems into self-maintaining infrastructure. HiFlight’s long-term competitive advantage is fundamentally driven by a learning loop. As more pipelines are managed, HiFlight continuously improves its company-specific ability to generate transformations, detect schema drifts, and resolve failures. This creates a system that becomes more accurate and efficient over time, similar to the way cursor and claude code work. In addition, HiFlight integrates ingestion, transformation, validation, and deployment into a single AI-native system, making it significantly more complex to replicate than existing solutions, which are also fairly fragmented. As adoption grows, switching costs increase, and companies rely on HiFlight’s continuously optimized pipelines and system intelligence. HiFlight’s execution strategy is to begin with early adopters, mostly startups, by offering onboarding credits and integration support with existing data warehouses. The platform is delivered as a usage-based SaaS product, scaling with data volume, pipeline complexity, and AI agent usage. Over time, the system will expand through additional connectors, enterprise features, and collaborative workflows (RBAC & clean-room datasets). As the founder, I bring experience in both data systems and applied AI through academic coursework in computer science and statistics at UC Berkeley and prior work in ELT and AI-driven infrastructure. During my internship at SciFin, I built end-to-end data pipelines, extracting customer data from multiple sources and loading it into Salesforce sandboxes. I also orchestrated complex DBT transformations structured as a DAG, such that the output of the first task was used as the input for the next and so on. Working extensively on data migration and transformations, I experienced firsthand how much time developers had to spend navigating CLI workflows, git branches and complex YAML files than actually analyzing the data itself. This combination uniquely positions me to build and scale an AI-native data platform.